code: def main(config: DictConfig) -> None:
      print(config)
      tokenizer = AutoTokenizer.from_pretrained("imthanhlv/gpt2news")
      tokenizer.pad_token = tokenizer.eos_token
      tokenizer.padding_side = "right"
      gpt2 = AutoModelWithLMHead.from_pretrained("imthanhlv/gpt2news")
      gpt2.resize_token_embeddings(len(tokenizer))
      gpt2.config.pad_token_id = gpt2.config.eos_token_id
  
      corpus = UITABSAHotel()
      # num_labels = corpus.num_labels
      num_labels = corpus.num_aspect_labels
      model = GPT2TextClassification(gpt2, num_labels)
      datamodule = MultiLabelClassificationDatamodule(
          corpus=corpus, tokenizer=tokenizer, **config.data
      )
      logger = WandbLogger(**config.logger)
      trainer = pl.Trainer(logger=logger, **config.trainer)
      trainer.fit(model, datamodule=datamodule)
  
file_path: underthesea/examples/sentiment/train_gpt2.py
imports: import hydra
  import pytorch_lightning
  from omegaconf import DictConfig
  from torch.optim import SGD
  import torch.nn
  from torchmetrics import F1Score
  from transformers import AutoTokenizer, AutoModelWithLMHead
  from pytorch_lightning.loggers import WandbLogger
  from examples.sentiment.data import MultiLabelClassificationDatamodule
  from underthesea.datasets.uit_absa_hotel.uit_absa_hotel import UITABSAHotel
label_desc: Use image node_id 1 for calling a global function with example usage: main(config) without return types